{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## CLIP Encoder\n",
    "\n",
    "**The encoder transforms all input sequences (e.g. the raw text user prompt) into embeddings.** \n",
    "\n",
    "Embeddings are vectors that represent the semantic \"meaning\" of each word in the text (ex. the words \"blue\" and \"ocean\" have vectors closer in value than the words \"cat\" and \"saturn\")."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 268,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 500,
       "width": 500
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from IPython.display import Image, display\n",
    "display(Image(filename='../../assets/Diffusion/4-emb.png', width=500, height=500))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The CLIP encoder is trained to embed images and text into the **same latent space**, so semantically similar images and text are close together in this space.\n",
    "\n",
    "Here, we will discuss the text encoder. The text encoder is a Transformer. For computational efficiency and maximal understanding, we will restrict the model base size and max sequence length."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The encoder takes the raw user prompt as input, and outputs:\n",
    "\n",
    "\n",
    " - **Sequence embeddings**: one embedding vector per token in the prompt, used for token-level attention in the transformer so each word can be attended to.\n",
    " - **Pooled embedding**: a single embedding vector representing the entire prompt, used for time conditioning."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Architecture overview:\n",
    "\n",
    "The CLIP text encoder architecture consists of:\n",
    "<br><br>\n",
    "\n",
    "1. [**Tokenization**](#1-tokenization)\n",
    "    <br><br>\n",
    "    **input**: raw user input\n",
    "- tokenize with BPE tokenizer\n",
    "- preprend with [SOS] tokens \n",
    "    - SOS: \"start of sequence\"\n",
    "    - The embedding at the **first** token position\n",
    "    - Used for the **pooled embedding**\n",
    "- truncate to fixed length (77 tokens)\n",
    "    <br>\n",
    "\n",
    "    **output**: list of tokens\n",
    "    ex. user prompt: `\"a red cat\"` -> `[\"a\", \"red\", \"cat\"]`\n",
    "\n",
    "<br>\n",
    "\n",
    "2. [**Embedding layer**](#2-embedding-layer)\n",
    "    <br><br>\n",
    "    **input:** list of tokens\n",
    "- each token is mapped to a lookup embedding (pretrained embeddings). i.e. each token is assigned their corresponding embedding in a \"generic\"/static/already-trained embedding space\n",
    "<br>\n",
    "ex. `[\"a red cat\"] -> [0.1, 0.8, 0.5]`\n",
    "- add positional embeddings\n",
    "    - since transformers don't know the order of tokens\n",
    "\n",
    "    **output**: static embeddings of user input's tokens with positional info/conditioning\n",
    "\n",
    "    <br>\n",
    "\n",
    "3. [**Transformer**](#3-transformer)\n",
    "    <br><br>\n",
    "    **input**: sum of token embeddings and positional embeddings (static embeddings of user input's tokens with token position info/conditioning)\n",
    "- multi-head self-attention layer. for each head,\n",
    "    - **computes queries (Q), keys (K) & values (V)**\n",
    "    - compute attention scores\n",
    "    - multiply scores by values\n",
    "    - concatenate all heads & apply final linear transformation\n",
    "- the attention output is added to the original input (residual connection)\n",
    "    - then passed through a layer norm\n",
    "- each token vector is passed through the same 2-layer MLP\n",
    "- apply another residual + layer norm\n",
    "    \n",
    "\n",
    "    **output**: sequence embeddings and pooled embedding\n",
    "\n",
    "    for more info on the transformer, see <a href=\"../../transformer/transformer_notes.ipynb\">transformer notes</a>\n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1. Tokenization"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We import & intialize the BPE tokenizer from HuggingFace"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 269,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Install required dependencies (run this if you get import errors)\n",
    "# !pip install transformers torch"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 270,
   "metadata": {},
   "outputs": [],
   "source": [
    "from transformers import CLIPTokenizerFast, CLIPTextModel\n",
    "\n",
    "embedding_dim = 512\n",
    "class CLIPTextCheckpoints:\n",
    "    def __init__(self, embedding_dim=embedding_dim):\n",
    "        self._tokenizer = CLIPTokenizerFast.from_pretrained(\"openai/clip-vit-large-patch14\")\n",
    "\n",
    "    def encode(self, text):\n",
    "        return self._tokenizer(text, return_tensors=\"pt\").input_ids\n",
    "\n",
    "pretrained = CLIPTextCheckpoints()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 271,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Prompt: 'a red cat'\n",
      "Token IDs: [49406, 320, 736, 2368, 49407]\n",
      "Number of tokens: 5\n",
      "Tokens: ['<|startoftext|>', 'a</w>', 'red</w>', 'cat</w>', '<|endoftext|>']...\n"
     ]
    }
   ],
   "source": [
    "prompt = \"a red cat\"\n",
    "print(f\"Prompt: '{prompt}'\")\n",
    "\n",
    "token_ids = pretrained.encode(prompt)\n",
    "\n",
    "# print(f\"Token IDs shape: {token_ids.shape}\")    # [batch size (# of sequences being processed at once), max sequence length (# of tokens in each sequence)]\n",
    "print(f\"Token IDs: {token_ids[0][:10].tolist()}\")\n",
    "print(f\"Number of tokens: {token_ids.shape[1]}\")\n",
    "\n",
    "# decode back to tokens to see what the tokenizer did (prepend <|startoftext|> and append <|endoftext|>)\n",
    "tokens = pretrained._tokenizer.convert_ids_to_tokens(token_ids[0])\n",
    "print(f\"Tokens: {tokens[:10]}...\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "While there are 3 words in the prompt (\"a\", \"red\", & \"cat\"), the tokenizer adds <|startoftext|> & <|endoftext|> tokens, resulting in 5 total tokens."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2. Embedding Layer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 272,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Embedding layer: Embedding(49408, 512)\n",
      "Token embeddings shape: torch.Size([1, 5, 512])\n",
      "Positional embeddings used: torch.Size([5, 512])\n",
      "Final positioned embeddings shape: torch.Size([1, 5, 512])\n"
     ]
    }
   ],
   "source": [
    "import torch\n",
    "import torch.nn as nn\n",
    "\n",
    "num_embeddings = pretrained._tokenizer.vocab_size\n",
    "embedding_dim = 512\n",
    "\n",
    "embedding_layer = torch.nn.Embedding(num_embeddings, embedding_dim)\n",
    "print(f\"Embedding layer: {embedding_layer}\")\n",
    "\n",
    "# positional embedding: order/position of tokens in the sequence (conditioning for the transformer)\n",
    "max_length = 77\n",
    "positional_embedding = nn.Parameter(torch.empty(max_length, embedding_dim))\n",
    "nn.init.normal_(positional_embedding, std=0.01)  # or use truncated normal\n",
    "\n",
    "# use the token IDs from our prompt \"a red cat\"\n",
    "input_ids = token_ids  # shape: [1, 5] - already tokenized above\n",
    "\n",
    "# (1) token embeddings\n",
    "token_embeddings = embedding_layer(input_ids)  # (batch, seq_len, dim)\n",
    "\n",
    "# (2) add positional embeddings (only use the length we need)\n",
    "seq_len = input_ids.shape[1]  # 5 tokens\n",
    "positioned = token_embeddings + positional_embedding[:seq_len].unsqueeze(0)  # (batch, seq_len, dim)\n",
    "\n",
    "if token_ids.shape[1] > max_length:\n",
    "    token_ids = token_ids[:, :max_length]\n",
    "else:\n",
    "    padding_length = max_length - token_ids.shape[1]\n",
    "    token_ids = torch.cat([token_ids, torch.zeros(1, padding_length, dtype=token_ids.dtype)], dim=1)\n",
    "\n",
    "print(f\"Token embeddings shape: {token_embeddings.shape}\")\n",
    "print(f\"Positional embeddings used: {positional_embedding[:seq_len].shape}\")\n",
    "print(f\"Final positioned embeddings shape: {positioned.shape}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we have the \"generic\"/static embeddings of the tokens of the user's input! Let's move on to the Transformer."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3. Transformer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 273,
   "metadata": {},
   "outputs": [],
   "source": [
    "import math\n",
    "    \n",
    "class MultiHeadAttention(nn.Module):\n",
    "    def __init__(self, embedding_dim, n_heads=8):\n",
    "        super().__init__()\n",
    "        self.embedding_dim = embedding_dim\n",
    "        self.n_heads = n_heads\n",
    "        self.head_dim = embedding_dim // n_heads\n",
    "\n",
    "        assert self.head_dim * n_heads == embedding_dim, \"embedding dimension must be divisible by number of heads\"\n",
    "\n",
    "        # linear projections for queries, keys, and values\n",
    "        self.W_q = nn.Linear(embedding_dim, embedding_dim)\n",
    "        self.W_k = nn.Linear(embedding_dim, embedding_dim)\n",
    "        self.W_v = nn.Linear(embedding_dim, embedding_dim)\n",
    "\n",
    "        # final linear projection\n",
    "        self.W_o = nn.Linear(embedding_dim, embedding_dim)\n",
    "        \n",
    "    \n",
    "    def forward(self, x):\n",
    "        batch_size, seq_len, _ = x.shape\n",
    "\n",
    "        # linear projections\n",
    "        q = self.W_q(x) # (batch_size, seq_len, embedding_dim)\n",
    "        k = self.W_k(x) # (batch_size, seq_len, embedding_dim)\n",
    "        v = self.W_v(x) # (batch_size, seq_len, embedding_dim)\n",
    "\n",
    "        # split into heads & transpose for attention computation\n",
    "        q = q.view(batch_size, seq_len, self.n_heads, self.head_dim).transpose(1, 2)\n",
    "        k = k.view(batch_size, seq_len, self.n_heads, self.head_dim).transpose(1, 2)\n",
    "        v = v.view(batch_size, seq_len, self.n_heads, self.head_dim).transpose(1, 2)\n",
    "\n",
    "        # compute attention scores (scaled dot product attention)\n",
    "        attention_scores = torch.matmul(q, k.transpose(-2, -1)) / math.sqrt(self.head_dim)\n",
    "        weights = torch.softmax(attention_scores, dim=-1)\n",
    "        attention = torch.matmul(weights, v)\n",
    "\n",
    "        # concatenate heads & transpose back\n",
    "        concat = attention.transpose(1, 2).contiguous().view(batch_size, seq_len, self.embedding_dim)\n",
    "\n",
    "        # final linear projection\n",
    "        out = self.W_o(concat)\n",
    "        return out\n",
    "    \n",
    "class TransformerBlock(nn.Module):\n",
    "    def __init__(self, embedding_dim, n_heads, dropout=0.0):\n",
    "        super().__init__()\n",
    "        self.ln1 = nn.LayerNorm(embedding_dim)\n",
    "        self.ln2 = nn.LayerNorm(embedding_dim)\n",
    "        self.attn = MultiHeadAttention(embedding_dim, 8)\n",
    "\n",
    "        # MLP block\n",
    "        mlp_dim = int(embedding_dim * 4)\n",
    "        self.mlp = nn.Sequential(\n",
    "            nn.Linear(embedding_dim, mlp_dim),\n",
    "            nn.GELU(),\n",
    "            nn.Dropout(dropout),\n",
    "            nn.Linear(mlp_dim, embedding_dim),\n",
    "            nn.Dropout(dropout)\n",
    "        )\n",
    "    \n",
    "    def forward(self, x):\n",
    "        # pre-norm residual connections in CLIP\n",
    "        x = x + self.attn(self.ln1(x))\n",
    "        x = x + self.mlp(self.ln2(x))\n",
    "        return x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 274,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Text transformer: CLIPTextEncoder(\n",
      "  (blocks): ModuleList(\n",
      "    (0-11): 12 x TransformerBlock(\n",
      "      (ln1): LayerNorm((512,), eps=1e-05, elementwise_affine=True)\n",
      "      (ln2): LayerNorm((512,), eps=1e-05, elementwise_affine=True)\n",
      "      (attn): MultiHeadAttention(\n",
      "        (W_q): Linear(in_features=512, out_features=512, bias=True)\n",
      "        (W_k): Linear(in_features=512, out_features=512, bias=True)\n",
      "        (W_v): Linear(in_features=512, out_features=512, bias=True)\n",
      "        (W_o): Linear(in_features=512, out_features=512, bias=True)\n",
      "      )\n",
      "      (mlp): Sequential(\n",
      "        (0): Linear(in_features=512, out_features=2048, bias=True)\n",
      "        (1): GELU(approximate='none')\n",
      "        (2): Dropout(p=0.0, inplace=False)\n",
      "        (3): Linear(in_features=2048, out_features=512, bias=True)\n",
      "        (4): Dropout(p=0.0, inplace=False)\n",
      "      )\n",
      "    )\n",
      "  )\n",
      "  (ln_final): LayerNorm((512,), eps=1e-05, elementwise_affine=True)\n",
      ")\n",
      "Transformer output shape: torch.Size([1, 5, 512])\n",
      "Final text features shape: torch.Size([512])\n",
      "Text features (first 5 values): tensor([-0.4445,  0.1461,  2.1911, -0.8701,  0.3039], grad_fn=<SliceBackward0>)\n"
     ]
    }
   ],
   "source": [
    "# complete CLIP text encoder\n",
    "\n",
    "class CLIPTextEncoder(nn.Module):\n",
    "    def __init__(self, embedding_dim=512, num_embeddings=49408, max_length=77, n_layers=12, n_heads=8, dropout=0.0):\n",
    "        super().__init__()\n",
    "        self.embedding_dim = embedding_dim\n",
    "        self.num_embeddings = num_embeddings\n",
    "        self.max_length = max_length\n",
    "        \n",
    "        # transformer blocks\n",
    "        self.blocks = nn.ModuleList([\n",
    "            TransformerBlock(embedding_dim, n_heads, dropout)\n",
    "            for _ in range(n_layers)\n",
    "        ])\n",
    "        \n",
    "        # final layer norm\n",
    "        self.ln_final = nn.LayerNorm(embedding_dim)\n",
    "        \n",
    "    def forward(self, input_ids):\n",
    "        batch_size, seq_len = input_ids.shape\n",
    "        \n",
    "        # apply transformer blocks\n",
    "        x = positioned\n",
    "        for block in self.blocks:\n",
    "            x = block(x)\n",
    "            \n",
    "        # final layer norm\n",
    "        x = self.ln_final(x)\n",
    "        \n",
    "        return x\n",
    "\n",
    "# initialize the transformer encoder\n",
    "encoder = CLIPTextEncoder(\n",
    "    embedding_dim=512,\n",
    "    num_embeddings=num_embeddings,\n",
    "    max_length=77, \n",
    "    n_layers=12, \n",
    "    n_heads=8, \n",
    "    dropout=0.0\n",
    ")\n",
    "\n",
    "print(f\"Text transformer: {encoder}\")\n",
    "\n",
    "# use the properly truncated/padded token_ids\n",
    "output = encoder(token_ids)\n",
    "print(f\"Transformer output shape: {output.shape}\")\n",
    "\n",
    "# for CLIP, typically use the embedding at the EOS token position\n",
    "# find EOS token position (end of text token)\n",
    "eos_token_id = pretrained._tokenizer.eos_token_id\n",
    "eos_positions = (token_ids == eos_token_id).nonzero(as_tuple=True)[1]\n",
    "\n",
    "# if no EOS found, use the last token position\n",
    "if len(eos_positions) == 0:\n",
    "    eos_positions = torch.tensor([token_ids.shape[1] - 1])\n",
    "\n",
    "# extract text representation at EOS position\n",
    "text_features = output[0, eos_positions[0]]\n",
    "print(f\"Final text features shape: {text_features.shape}\")\n",
    "print(f\"Text features (first 5 values): {text_features[:5]}\")\n",
    "\n",
    "# alternative: you can also use the last token position or mean pooling\n",
    "# last_token_features = output[:, -1]  # use last token\n",
    "# mean_pooled_features = output.mean(dim=1)  # mean pooling "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Testing!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 275,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loading reference CLIP model...\n",
      "Reference model vocab size: 49408\n",
      "Custom model vocab size: 49408\n",
      "\n",
      "1. ARCHITECTURE VALIDATION\n",
      "----------------------------------------\n",
      "Reference model parameters: 123,060,480\n",
      "Custom model parameters: 37,829,632\n",
      "Parameter difference: 85,230,848\n",
      "\n",
      "2. TOKENIZATION VALIDATION\n",
      "----------------------------------------\n",
      "\n",
      "Prompt 1: 'a red cat'\n",
      "  Input shape: torch.Size([1, 77])\n",
      "  Token IDs: [49406, 320, 736, 2368, 49407, 49407, 49407, 49407, 49407, 49407]...\n",
      "  Decoded tokens: ['<|startoftext|>', 'a</w>', 'red</w>', 'cat</w>', '<|endoftext|>', '<|endoftext|>', '<|endoftext|>', '<|endoftext|>', '<|endoftext|>', '<|endoftext|>']...\n",
      "  Start token positions: [0]\n",
      "  End token positions: [4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76]\n",
      "\n",
      "Prompt 2: 'a beautiful sunset over mountains'\n",
      "  Input shape: torch.Size([1, 77])\n",
      "  Token IDs: [49406, 320, 1215, 3424, 962, 5873, 49407, 49407, 49407, 49407]...\n",
      "  Decoded tokens: ['<|startoftext|>', 'a</w>', 'beautiful</w>', 'sunset</w>', 'over</w>', 'mountains</w>', '<|endoftext|>', '<|endoftext|>', '<|endoftext|>', '<|endoftext|>']...\n",
      "  Start token positions: [0]\n",
      "  End token positions: [6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76]\n",
      "\n",
      "Prompt 3: 'abstract geometric patterns'\n",
      "  Input shape: torch.Size([1, 77])\n",
      "  Token IDs: [49406, 10197, 22419, 11637, 49407, 49407, 49407, 49407, 49407, 49407]...\n",
      "  Decoded tokens: ['<|startoftext|>', 'abstract</w>', 'geometric</w>', 'patterns</w>', '<|endoftext|>', '<|endoftext|>', '<|endoftext|>', '<|endoftext|>', '<|endoftext|>', '<|endoftext|>']...\n",
      "  Start token positions: [0]\n",
      "  End token positions: [4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76]\n",
      "\n",
      "3. OUTPUT SHAPE VALIDATION\n",
      "----------------------------------------\n",
      "Reference last_hidden_state shape: torch.Size([1, 77, 768])\n",
      "Reference pooler_output shape: torch.Size([1, 768])\n",
      "Custom model output shape: torch.Size([1, 5, 512])\n",
      "Custom pooled output shape: torch.Size([512])\n",
      "\n",
      "4. ATTENTION PATTERN VALIDATION\n",
      "----------------------------------------\n",
      "Short input output range: [-3.4733, 4.0238]\n",
      "Long input output range: [-3.4733, 4.0238]\n",
      "Short output has NaN: False\n",
      "Short output has Inf: False\n",
      "Long output has NaN: False\n",
      "Long output has Inf: False\n",
      "\n",
      "5. GRADIENT FLOW VALIDATION\n",
      "----------------------------------------\n",
      "Gradient norms - Min: 0.000000, Max: 113.137085, Mean: 0.959241\n",
      "\n",
      "6. CONSISTENCY VALIDATION\n",
      "----------------------------------------\n",
      "Max difference between run 1 and 2: 0.0000000000\n",
      "Max difference between run 2 and 3: 0.0000000000\n",
      "✓ Model outputs are consistent\n",
      "\n",
      "7. EDGE CASES VALIDATION\n",
      "----------------------------------------\n",
      "  ✓ '...' - Shape: torch.Size([1, 5, 512])\n",
      "  ✓ ' ...' - Shape: torch.Size([1, 5, 512])\n",
      "  ✓ 'aaaaaaaaaaaaaaaaaaaa...' - Shape: torch.Size([1, 5, 512])\n",
      "  ✓ '🚀🎉🌟...' - Shape: torch.Size([1, 5, 512])\n",
      "  ✓ '123 456 789...' - Shape: torch.Size([1, 5, 512])\n",
      "\n",
      "============================================================\n",
      "VALIDATION COMPLETE\n",
      "============================================================\n",
      "\n",
      "SUMMARY OF CHECKS:\n",
      "1. ✓ Architecture validation - parameter count comparison\n",
      "2. ✓ Tokenization validation - special tokens, encoding/decoding\n",
      "3. ✓ Output shape validation - tensor dimensions\n",
      "4. ✓ Attention pattern validation - NaN/Inf checks\n",
      "5. ✓ Gradient flow validation - backprop functionality\n",
      "6. ✓ Consistency validation - deterministic outputs\n",
      "7. ✓ Edge cases validation - boundary conditions\n",
      "\n",
      "To achieve 100% correctness, you would also need to:\n",
      "- Load pre-trained weights from the official CLIP model\n",
      "- Compare numerical outputs with reference implementation\n",
      "- Test on CLIP's standard evaluation datasets\n",
      "- Verify attention weights match the reference model\n"
     ]
    }
   ],
   "source": [
    "def comprehensive_clip_test():\n",
    "\n",
    "    # initialize tokenizer\n",
    "    tokenizer = CLIPTokenizerFast.from_pretrained(\"openai/clip-vit-large-patch14\")\n",
    "    \n",
    "    # test cases\n",
    "    test_prompts = [\n",
    "        \"a red cat\",\n",
    "        \"a beautiful sunset over mountains\",\n",
    "        \"abstract geometric patterns\",\n",
    "        \"portrait of a young woman\",\n",
    "        \"\",  # empty string\n",
    "        \"a\" * 100,  # very long string\n",
    "        \"The quick brown fox jumps over the lazy dog\",\n",
    "        \"日本語のテキスト\",  # non-english text\n",
    "    ]\n",
    "    \n",
    "    # load reference CLIP model\n",
    "    print(\"Loading reference CLIP model...\")\n",
    "    reference_model = CLIPTextModel.from_pretrained(\"openai/clip-vit-large-patch14\")\n",
    "    reference_model.eval()\n",
    "    \n",
    "    # load our model\n",
    "    encoder.eval()\n",
    "    \n",
    "    print(f\"Reference model vocab size: {reference_model.text_model.embeddings.token_embedding.num_embeddings}\")\n",
    "    print(f\"Custom model vocab size: {encoder.num_embeddings}\")\n",
    "    \n",
    "    # test 1: architecture validation\n",
    "    print(\"\\n1. ARCHITECTURE VALIDATION\")\n",
    "    print(\"-\" * 40)\n",
    "    \n",
    "    def count_parameters(model):\n",
    "        return sum(p.numel() for p in model.parameters() if p.requires_grad)\n",
    "    \n",
    "    ref_params = count_parameters(reference_model)\n",
    "    custom_params = count_parameters(encoder)\n",
    "    \n",
    "    print(f\"Reference model parameters: {ref_params:,}\")\n",
    "    print(f\"Custom model parameters: {custom_params:,}\")\n",
    "    print(f\"Parameter difference: {abs(ref_params - custom_params):,}\")\n",
    "    \n",
    "    # test 2: tokenization validation\n",
    "    print(\"\\n2. TOKENIZATION VALIDATION\")\n",
    "    print(\"-\" * 40)\n",
    "    \n",
    "    for i, prompt in enumerate(test_prompts[:3]):  # test first 3 prompts\n",
    "        print(f\"\\nPrompt {i+1}: '{prompt}'\")\n",
    "        \n",
    "        # tokenize\n",
    "        tokens = tokenizer(prompt, return_tensors=\"pt\", max_length=77, padding=\"max_length\", truncation=True)\n",
    "        input_ids = tokens.input_ids\n",
    "        \n",
    "        print(f\"  Input shape: {input_ids.shape}\")\n",
    "        print(f\"  Token IDs: {input_ids[0][:10].tolist()}...\")\n",
    "        \n",
    "        # decode back\n",
    "        decoded_tokens = tokenizer.convert_ids_to_tokens(input_ids[0])\n",
    "        print(f\"  Decoded tokens: {decoded_tokens[:10]}...\")\n",
    "        \n",
    "        # check special tokens\n",
    "        start_token_pos = (input_ids == tokenizer.bos_token_id).nonzero(as_tuple=True)\n",
    "        end_token_pos = (input_ids == tokenizer.eos_token_id).nonzero(as_tuple=True)\n",
    "        \n",
    "        print(f\"  Start token positions: {start_token_pos[1].tolist() if len(start_token_pos[1]) > 0 else 'None'}\")\n",
    "        print(f\"  End token positions: {end_token_pos[1].tolist() if len(end_token_pos[1]) > 0 else 'None'}\")\n",
    "    \n",
    "    # test 3: output Shape Validation\n",
    "    print(\"\\n3. OUTPUT SHAPE VALIDATION\")\n",
    "    print(\"-\" * 40)\n",
    "    \n",
    "    test_input = tokenizer(\"a red cat\", return_tensors=\"pt\", max_length=77, padding=\"max_length\", truncation=True)\n",
    "    \n",
    "    with torch.no_grad():\n",
    "        # reference model output\n",
    "        ref_output = reference_model(**test_input)\n",
    "        ref_last_hidden = ref_output.last_hidden_state\n",
    "        ref_pooled = ref_output.pooler_output\n",
    "        \n",
    "        # custom model output\n",
    "        custom_output = encoder(test_input.input_ids)\n",
    "        \n",
    "        print(f\"Reference last_hidden_state shape: {ref_last_hidden.shape}\")\n",
    "        print(f\"Reference pooler_output shape: {ref_pooled.shape}\")\n",
    "        print(f\"Custom model output shape: {custom_output.shape}\")\n",
    "        \n",
    "        # for CLIP, get the EOS token representation\n",
    "        eos_token_id = tokenizer.eos_token_id\n",
    "        eos_positions = (test_input.input_ids == eos_token_id).nonzero(as_tuple=True)[1]\n",
    "        \n",
    "        if len(eos_positions) > 0:\n",
    "            custom_pooled = custom_output[0, eos_positions[0]]\n",
    "            print(f\"Custom pooled output shape: {custom_pooled.shape}\")\n",
    "    \n",
    "    # test 4: attention pattern validation\n",
    "    print(\"\\n4. ATTENTION PATTERN VALIDATION\")\n",
    "    print(\"-\" * 40)\n",
    "    \n",
    "    # test attention patterns with different sequence lengths\n",
    "    short_input = tokenizer(\"cat\", return_tensors=\"pt\", max_length=77, padding=\"max_length\", truncation=True)\n",
    "    long_input = tokenizer(\"a very long sentence with many words to test attention patterns\", \n",
    "                          return_tensors=\"pt\", max_length=77, padding=\"max_length\", truncation=True)\n",
    "    \n",
    "    with torch.no_grad():\n",
    "        short_output = encoder(short_input.input_ids)\n",
    "        long_output = encoder(long_input.input_ids)\n",
    "        \n",
    "        print(f\"Short input output range: [{short_output.min():.4f}, {short_output.max():.4f}]\")\n",
    "        print(f\"Long input output range: [{long_output.min():.4f}, {long_output.max():.4f}]\")\n",
    "        \n",
    "        # check for NaN or infinite values\n",
    "        print(f\"Short output has NaN: {torch.isnan(short_output).any()}\")\n",
    "        print(f\"Short output has Inf: {torch.isinf(short_output).any()}\")\n",
    "        print(f\"Long output has NaN: {torch.isnan(long_output).any()}\")\n",
    "        print(f\"Long output has Inf: {torch.isinf(long_output).any()}\")\n",
    "    \n",
    "    # test 5: gradient flow validation\n",
    "    print(\"\\n5. GRADIENT FLOW VALIDATION\")\n",
    "    print(\"-\" * 40)\n",
    "    \n",
    "    encoder.train()\n",
    "    test_input = tokenizer(\"test gradient flow\", return_tensors=\"pt\", max_length=77, padding=\"max_length\", truncation=True)\n",
    "    \n",
    "    # forward pass\n",
    "    output = encoder(test_input.input_ids)\n",
    "    loss = output.sum()  # dummy loss\n",
    "    \n",
    "    # backward pass\n",
    "    loss.backward()\n",
    "    \n",
    "    # check gradients\n",
    "    grad_norms = []\n",
    "    for name, param in encoder.named_parameters():\n",
    "        if param.grad is not None:\n",
    "            grad_norm = param.grad.norm().item()\n",
    "            grad_norms.append(grad_norm)\n",
    "            if grad_norm == 0:\n",
    "                print(f\"  WARNING: Zero gradient for {name}\")\n",
    "    \n",
    "    print(f\"Gradient norms - Min: {min(grad_norms):.6f}, Max: {max(grad_norms):.6f}, Mean: {np.mean(grad_norms):.6f}\")\n",
    "    \n",
    "    # test 6: consistency validation\n",
    "    print(\"\\n6. CONSISTENCY VALIDATION\")\n",
    "    print(\"-\" * 40)\n",
    "    \n",
    "    encoder.eval()\n",
    "    \n",
    "    # test same input multiple times\n",
    "    test_input = tokenizer(\"consistency test\", return_tensors=\"pt\", max_length=77, padding=\"max_length\", truncation=True)\n",
    "    \n",
    "    outputs = []\n",
    "    for i in range(3):\n",
    "        with torch.no_grad():\n",
    "            output = encoder(test_input.input_ids)\n",
    "            outputs.append(output)\n",
    "    \n",
    "    # check consistency\n",
    "    diff_1_2 = torch.abs(outputs[0] - outputs[1]).max().item()\n",
    "    diff_2_3 = torch.abs(outputs[1] - outputs[2]).max().item()\n",
    "    \n",
    "    print(f\"Max difference between run 1 and 2: {diff_1_2:.10f}\")\n",
    "    print(f\"Max difference between run 2 and 3: {diff_2_3:.10f}\")\n",
    "    \n",
    "    if diff_1_2 < 1e-6 and diff_2_3 < 1e-6:\n",
    "        print(\"✓ Model outputs are consistent\")\n",
    "    else:\n",
    "        print(\"✗ Model outputs are inconsistent\")\n",
    "    \n",
    "    # test 7: edge cases\n",
    "    print(\"\\n7. EDGE CASES VALIDATION\")\n",
    "    print(\"-\" * 40)\n",
    "    \n",
    "    edge_cases = [\n",
    "        \"\",  # empty string\n",
    "        \" \",  # single space\n",
    "        \"a\" * 200,  # very long string (will be truncated)\n",
    "        \"🚀🎉🌟\",  # emojis\n",
    "        \"123 456 789\",  # numbers\n",
    "    ]\n",
    "    \n",
    "    for case in edge_cases:\n",
    "        try:\n",
    "            tokens = tokenizer(case, return_tensors=\"pt\", max_length=77, padding=\"max_length\", truncation=True)\n",
    "            with torch.no_grad():\n",
    "                output = encoder(tokens.input_ids)\n",
    "            print(f\"  ✓ '{case[:20]}...' - Shape: {output.shape}\")\n",
    "        except Exception as e:\n",
    "            print(f\"  ✗ '{case[:20]}...' - Error: {e}\")\n",
    "    \n",
    "    print(\"\\n\" + \"=\" * 60)\n",
    "    print(\"VALIDATION COMPLETE\")\n",
    "    print(\"=\" * 60)\n",
    "    \n",
    "    # summary\n",
    "    print(\"\\nSUMMARY OF CHECKS:\")\n",
    "    print(\"1. ✓ Architecture validation - parameter count comparison\")\n",
    "    print(\"2. ✓ Tokenization validation - special tokens, encoding/decoding\")\n",
    "    print(\"3. ✓ Output shape validation - tensor dimensions\")\n",
    "    print(\"4. ✓ Attention pattern validation - NaN/Inf checks\")\n",
    "    print(\"5. ✓ Gradient flow validation - backprop functionality\")\n",
    "    print(\"6. ✓ Consistency validation - deterministic outputs\")\n",
    "    print(\"7. ✓ Edge cases validation - boundary conditions\")\n",
    "    \n",
    "    print(\"\\nTo achieve 100% correctness, you would also need to:\")\n",
    "    print(\"- Load pre-trained weights from the official CLIP model\")\n",
    "    print(\"- Compare numerical outputs with reference implementation\")\n",
    "    print(\"- Test on CLIP's standard evaluation datasets\")\n",
    "    print(\"- Verify attention weights match the reference model\")\n",
    "\n",
    "if __name__ == \"__main__\":\n",
    "    comprehensive_clip_test()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Extra - More rigorous implementation of the CLIP encoder"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 276,
   "metadata": {},
   "outputs": [],
   "source": [
    "# SPDX-FileCopyrightText: © 2025 Tenstorrent AI ULC\n",
    "# SPDX-License-Identifier: Apache-2.0\n",
    "\n",
    "import torch\n",
    "from dataclasses import dataclass\n",
    "\n",
    "\n",
    "@dataclass\n",
    "class CLIPTextConfig:\n",
    "    vocab_size: int\n",
    "    hidden_size: int\n",
    "    intermediate_size: int\n",
    "    num_hidden_layers: int\n",
    "    num_heads: int\n",
    "    max_position_embeddings: int\n",
    "    layer_norm_eps: float\n",
    "    attention_dropout: float\n",
    "\n",
    "\n",
    "# adapted from https://github.com/huggingface/transformers/blob/v4.47.0/src/transformers/models/clip/modeling_clip.py\n",
    "# ensure tokens can only attend to previous tokens\n",
    "def _create_4d_causal_attention_mask(input_shape, dtype, device):\n",
    "    batch_size, tgt_len = input_shape\n",
    "    mask = torch.full((tgt_len, tgt_len), torch.finfo(dtype).min, device=device)\n",
    "    mask_cond = torch.arange(mask.size(-1), device=device)\n",
    "    mask.masked_fill_(mask_cond < (mask_cond + 1).view(mask.size(-1), 1), 0)\n",
    "    mask = mask.to(dtype)\n",
    "    return mask[None, None, :, :].expand(batch_size, 1, tgt_len, tgt_len)\n",
    "\n",
    "\n",
    "# adapted from https://github.com/huggingface/transformers/blob/v4.47.0/src/transformers/models/clip/modeling_clip.py\n",
    "class CLIPTextEmbeddings(torch.nn.Module):\n",
    "    def __init__(self, config: CLIPTextConfig):\n",
    "        super().__init__()\n",
    "        embed_dim = config.hidden_size\n",
    "\n",
    "        # initialize embedding lookup tables\n",
    "        self.token_embedding = torch.nn.Embedding(config.vocab_size, embed_dim)\n",
    "        self.position_embedding = torch.nn.Embedding(config.max_position_embeddings, embed_dim)\n",
    "        self.register_buffer(\n",
    "            \"position_ids\", torch.arange(config.max_position_embeddings).expand((1, -1)), persistent=False\n",
    "        )\n",
    "\n",
    "    def forward(self, input_ids: torch.LongTensor) -> torch.Tensor:\n",
    "        seq_length = input_ids.shape[-1]\n",
    "        position_ids: torch.Tensor = self.position_ids[:, :seq_length]\n",
    "\n",
    "        # embed tokens and add position embeddings\n",
    "        inputs_embeds = self.token_embedding(input_ids)\n",
    "        position_embeddings = self.position_embedding(position_ids)\n",
    "        embeddings = inputs_embeds + position_embeddings\n",
    "\n",
    "        return embeddings\n",
    "\n",
    "\n",
    "# adapted from https://github.com/huggingface/transformers/blob/v4.47.0/src/transformers/models/clip/modeling_clip.py\n",
    "class CLIPAttention(torch.nn.Module):\n",
    "    def __init__(self, config: CLIPTextConfig):\n",
    "        super().__init__()\n",
    "        self.embed_dim = config.hidden_size\n",
    "        self.num_heads = config.num_heads\n",
    "        self.head_dim = self.embed_dim // self.num_heads\n",
    "        # if self.head_dim * self.num_heads != self.embed_dim:\n",
    "        #     raise ValueError(\n",
    "        #         f\"embed_dim must be divisible by num_heads (got `embed_dim`: {self.embed_dim} and `num_heads`: {self.num_heads}).\"\n",
    "        #     )\n",
    "        self.scale = self.head_dim**-0.5\n",
    "        self.dropout = config.attention_dropout\n",
    "\n",
    "        self.W_k = torch.nn.Linear(self.embed_dim, self.embed_dim)\n",
    "        self.W_v = torch.nn.Linear(self.embed_dim, self.embed_dim)\n",
    "        self.W_q = torch.nn.Linear(self.embed_dim, self.embed_dim)\n",
    "        self.W_o = torch.nn.Linear(self.embed_dim, self.embed_dim)\n",
    "\n",
    "    def _shape(self, tensor: torch.Tensor, seq_len: int, batch_size: int):\n",
    "        return tensor.view(batch_size, seq_len, self.num_heads, self.head_dim).transpose(1, 2).contiguous()\n",
    "\n",
    "    def forward(self, hidden_states: torch.Tensor, causal_attention_mask: torch.Tensor) -> torch.Tensor:\n",
    "        batch_size, seq_len, _ = hidden_states.shape\n",
    "\n",
    "        q = self.W_q(hidden_states) * self.scale\n",
    "        k = self.W_k(hidden_states)\n",
    "        v = self.W_v(hidden_states)\n",
    "\n",
    "        q = self._shape(q, seq_len, batch_size)\n",
    "        k = self._shape(k, seq_len, batch_size)\n",
    "        v = self._shape(v, seq_len, batch_size)\n",
    "\n",
    "        # reshape for batch matmul\n",
    "        proj_shape = (batch_size * self.num_heads, -1, self.head_dim)\n",
    "        query_states = q.view(*proj_shape)\n",
    "        key_states = k.view(*proj_shape)\n",
    "        value_states = v.view(*proj_shape)\n",
    "\n",
    "        attn_weights = torch.bmm(query_states, key_states.transpose(1, 2))\n",
    "\n",
    "        # apply causal mask\n",
    "        if causal_attention_mask is not None:\n",
    "            attn_weights = attn_weights.view(batch_size, self.num_heads, seq_len, seq_len) + causal_attention_mask\n",
    "            attn_weights = attn_weights.view(batch_size * self.num_heads, seq_len, seq_len)\n",
    "\n",
    "        attn_weights = torch.nn.functional.softmax(attn_weights, dim=-1)\n",
    "        attn_probs = torch.nn.functional.dropout(attn_weights, p=self.dropout, training=self.training)\n",
    "\n",
    "        attn_output = torch.bmm(attn_probs, value_states)\n",
    "        attn_output = attn_output.view(batch_size, self.num_heads, seq_len, self.head_dim)\n",
    "        attn_output = attn_output.transpose(1, 2)\n",
    "        attn_output = attn_output.reshape(batch_size, seq_len, self.embed_dim)\n",
    "\n",
    "        attn_output = self.W_o(attn_output)\n",
    "        return attn_output\n",
    "\n",
    "\n",
    "class CLIPMLP(torch.nn.Module):\n",
    "    def __init__(self, config: CLIPTextConfig):\n",
    "        super().__init__()\n",
    "        self.config = config\n",
    "        self.activation_fn = torch.nn.GELU()\n",
    "        self.fc1 = torch.nn.Linear(config.hidden_size, config.intermediate_size)\n",
    "        self.fc2 = torch.nn.Linear(config.intermediate_size, config.hidden_size)\n",
    "\n",
    "    def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:\n",
    "        hidden_states = self.fc1(hidden_states)\n",
    "        hidden_states = self.activation_fn(hidden_states)\n",
    "        hidden_states = self.fc2(hidden_states)\n",
    "        return hidden_states\n",
    "\n",
    "\n",
    "class CLIPEncoderLayer(torch.nn.Module):\n",
    "    def __init__(self, config: CLIPTextConfig):\n",
    "        super().__init__()\n",
    "        self.embed_dim = config.hidden_size\n",
    "        self.self_attn = CLIPAttention(config)\n",
    "        self.layer_norm1 = torch.nn.LayerNorm(self.embed_dim, eps=config.layer_norm_eps)\n",
    "        self.mlp = CLIPMLP(config)\n",
    "        self.layer_norm2 = torch.nn.LayerNorm(self.embed_dim, eps=config.layer_norm_eps)\n",
    "\n",
    "    def forward(self, hidden_states: torch.Tensor, causal_attention_mask: torch.Tensor) -> torch.Tensor:\n",
    "        residual = hidden_states\n",
    "\n",
    "        hidden_states = self.layer_norm1(hidden_states)\n",
    "        hidden_states = self.self_attn(hidden_states, causal_attention_mask)\n",
    "        hidden_states = residual + hidden_states\n",
    "\n",
    "        residual = hidden_states\n",
    "        hidden_states = self.layer_norm2(hidden_states)\n",
    "        hidden_states = self.mlp(hidden_states)\n",
    "        hidden_states = residual + hidden_states\n",
    "\n",
    "        return hidden_states\n",
    "\n",
    "\n",
    "class CLIPEncoder(torch.nn.Module):\n",
    "    def __init__(self, config: CLIPTextConfig):\n",
    "        super().__init__()\n",
    "        self.config = config\n",
    "        self.layers = torch.nn.ModuleList([CLIPEncoderLayer(config) for _ in range(config.num_hidden_layers)])\n",
    "\n",
    "    def forward(self, inputs_embeds: torch.Tensor, causal_attention_mask: torch.Tensor) -> torch.Tensor:\n",
    "        hidden_states = inputs_embeds\n",
    "        for encoder_layer in self.layers:\n",
    "            hidden_states = encoder_layer(hidden_states, causal_attention_mask)\n",
    "        return hidden_states\n",
    "\n",
    "\n",
    "class CLIPTextTransformer(torch.nn.Module):\n",
    "    def __init__(self, config: CLIPTextConfig):\n",
    "        super().__init__()\n",
    "        self.config = config\n",
    "        embed_dim = config.hidden_size\n",
    "        self.embeddings = CLIPTextEmbeddings(config)\n",
    "        # self.embeddings)\n",
    "        self.encoder = CLIPEncoder(config)\n",
    "        # print(self.encoder)\n",
    "        self.final_layer_norm = torch.nn.LayerNorm(embed_dim, eps=config.layer_norm_eps)\n",
    "\n",
    "    def forward(self, input_ids: torch.Tensor) -> tuple[torch.Tensor, torch.Tensor]:\n",
    "        input_shape = input_ids.shape\n",
    "        input_ids = input_ids.view(-1, input_shape[-1])\n",
    "\n",
    "        hidden_states = self.embeddings(input_ids=input_ids)\n",
    "\n",
    "        # create causal attention mask\n",
    "        causal_attention_mask = _create_4d_causal_attention_mask(\n",
    "            input_shape, hidden_states.dtype, device=hidden_states.device\n",
    "        )\n",
    "\n",
    "        encoder_outputs = self.encoder(\n",
    "            inputs_embeds=hidden_states,\n",
    "            causal_attention_mask=causal_attention_mask,\n",
    "        )\n",
    "\n",
    "        # sequence embedding output\n",
    "        last_hidden_state = encoder_outputs\n",
    "        last_hidden_state = self.final_layer_norm(last_hidden_state)\n",
    "\n",
    "        # pooled embedding output - single vector per sequence\n",
    "        pooled_output = last_hidden_state[\n",
    "            torch.arange(last_hidden_state.shape[0], device=last_hidden_state.device),\n",
    "            input_ids.to(dtype=torch.int, device=last_hidden_state.device).argmax(dim=-1),\n",
    "        ]\n",
    "        # print(\"POOLED\" + str(pooled_output.shape))\n",
    "        # print(\"LAST HIDDEN STATE\" + str(last_hidden_state.shape))\n",
    "        return last_hidden_state, pooled_output\n",
    "\n",
    "\n",
    "class CLIPTextEncoder(torch.nn.Module):\n",
    "    def __init__(self, config: CLIPTextConfig):\n",
    "        super().__init__()\n",
    "        self.text_model = CLIPTextTransformer(config)\n",
    "\n",
    "    def encode(self, input_ids: torch.Tensor) -> tuple[torch.Tensor, torch.Tensor]:\n",
    "        return self.text_model(input_ids)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 278,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(tensor([[[-0.5351,  0.7760, -0.2877,  ..., -2.4105,  0.4160,  0.6951],\n",
       "          [ 2.4617, -0.6049, -0.4992,  ..., -0.0044,  0.4356,  0.4625],\n",
       "          [ 1.6305, -0.9606,  0.7220,  ..., -0.7573, -1.0553,  0.7940],\n",
       "          [ 1.2313, -0.7158, -0.4900,  ...,  1.2796,  0.0928,  0.7564],\n",
       "          [ 1.3955, -0.7503, -0.0514,  ..., -0.0655,  1.3148,  0.1695]]],\n",
       "        grad_fn=<NativeLayerNormBackward0>),\n",
       " tensor([[ 1.3955, -0.7503, -0.0514, -0.3243,  1.5050, -0.1483,  1.7848, -0.4031,\n",
       "          -0.1634, -1.4319,  1.7125,  0.4143, -1.0465, -2.1336,  1.0621,  0.3320,\n",
       "          -0.8503, -1.6570,  0.3624,  0.9163, -0.1357, -0.0391, -0.0272,  1.8643,\n",
       "           0.7517, -0.9433, -0.1269,  0.9597,  1.9446, -0.1428,  0.3513,  0.9434,\n",
       "          -0.4236,  0.4243,  0.4864,  0.9471, -0.4170, -0.4951, -2.1305, -1.2157,\n",
       "           1.0569,  0.5885,  0.8586,  0.3228, -0.0581, -1.2124,  0.8291,  0.4944,\n",
       "          -0.7604, -0.0107,  0.1990, -0.3402, -1.1777,  1.4508,  1.7889, -2.1682,\n",
       "          -0.3368, -0.2867, -0.1956,  2.3199, -0.4969,  0.0810, -1.6714,  1.5833,\n",
       "           1.6761, -0.2407,  0.1352, -0.3878, -2.1247,  0.3533, -0.4053,  0.0741,\n",
       "          -0.1418, -1.8562,  0.5761,  0.5406,  0.6374, -0.8995, -0.1568,  1.1049,\n",
       "          -0.4910,  1.4999,  0.1311, -1.2689, -0.0708,  0.3930, -0.6227, -3.0763,\n",
       "           1.3056, -0.4345, -0.3162,  0.8841, -0.2871, -1.9804,  1.0990,  1.4373,\n",
       "           0.9214,  0.1747,  0.1024, -0.4149,  0.3645,  1.5028, -1.7370, -0.5297,\n",
       "           1.1518,  1.6815,  0.5549,  1.4993, -1.2490, -0.4783,  0.2831, -0.3403,\n",
       "          -0.4304,  0.3385,  0.9560, -0.3680, -0.2280,  1.1112,  0.5852, -0.7530,\n",
       "          -0.2524,  0.4760,  0.9070, -0.8907, -0.9754, -1.2415, -1.4624, -1.0738,\n",
       "          -0.1157, -0.5185, -1.2692,  0.6940,  0.4077, -0.5052, -1.2718, -0.1463,\n",
       "           0.3704,  0.2338,  0.9061, -0.6932,  1.1189, -0.0942, -2.0699,  0.3190,\n",
       "           0.4629, -0.3929,  0.4295,  0.1022, -0.9842,  0.4816, -1.4884, -1.1767,\n",
       "           0.6161,  0.7254,  0.4064,  0.2281,  0.3051,  1.6026, -0.4542,  0.6096,\n",
       "          -0.3002,  0.7114,  0.2734, -1.1789, -0.0812, -0.7333,  0.4020, -0.6409,\n",
       "          -0.4595, -1.6185,  0.4425, -1.9955,  0.5332, -0.0949, -0.0200, -0.7032,\n",
       "           0.7002,  0.5064, -0.6044,  0.1807,  1.0244, -1.9913,  1.0532, -1.5223,\n",
       "           0.0548,  0.5838,  1.5942, -1.2364,  0.0553, -0.0174, -0.3785, -0.6103,\n",
       "           1.2628, -0.0721,  1.6715, -0.4623, -1.8181, -0.7433, -0.6055,  1.1649,\n",
       "           1.5706, -1.7016, -0.3588,  0.2813,  1.1713,  0.5892, -0.4815, -1.0682,\n",
       "          -0.4397, -0.3445, -1.7924,  1.1291,  0.8052, -0.2521,  0.4109, -2.0964,\n",
       "           0.2005,  1.3473, -0.9503, -2.0903, -0.6094,  0.1188, -1.4679, -0.7049,\n",
       "           0.9620, -0.3061, -0.9113, -0.3259,  0.6811, -0.3464,  0.6598, -1.0993,\n",
       "          -1.4714, -1.0383,  1.2106, -0.7740, -1.5898, -1.0291,  1.1053, -0.4693,\n",
       "           1.4318, -0.0981,  1.9253, -0.1573,  1.4752, -0.1175, -0.5510,  2.0041,\n",
       "           1.4314,  1.0549,  0.1633, -1.2085,  0.8896,  0.0492, -0.8364,  1.0696,\n",
       "          -1.0979,  3.2957,  0.3386,  0.1936,  0.1556, -0.1777,  0.6787,  1.3983,\n",
       "          -0.1567, -1.0520,  0.0461, -1.4938,  0.9314, -0.3864,  0.9496,  1.0087,\n",
       "           0.6759,  0.9490, -0.5607,  0.3938, -0.4414, -2.0922, -0.4554, -0.2174,\n",
       "          -0.8561, -0.6890,  0.4881,  0.7274, -1.0817,  1.1952, -0.0853,  0.1877,\n",
       "           0.2454, -0.3057,  0.6173, -1.0575,  1.1213,  0.2203, -0.2865, -0.4135,\n",
       "          -0.9318, -0.6818, -0.9289, -1.1168, -0.3187,  0.9956, -0.4309, -1.9733,\n",
       "           0.1025,  1.3388,  1.3038, -0.4525,  0.7224,  2.3345, -1.8001, -1.4840,\n",
       "           2.6439, -1.4516, -1.0721,  1.5155, -0.3735,  1.7549, -0.4982, -0.6002,\n",
       "          -0.5754,  0.7181,  1.5203, -1.3081, -1.1895,  2.0137, -1.0264, -0.4896,\n",
       "          -0.7315,  0.4405, -0.8660, -0.1590, -1.4232, -0.6417,  0.7436, -0.9692,\n",
       "          -1.6256,  0.3985,  0.3043,  0.1134, -0.5213,  1.4823,  0.0744, -0.2368,\n",
       "          -0.3898,  0.0555,  0.8043, -0.8460, -3.1456, -0.4329, -1.3932, -1.1610,\n",
       "          -0.1193, -0.0196, -0.2739, -0.0254,  0.5648, -0.3368,  1.4259, -0.8812,\n",
       "           2.1465, -0.8382,  2.3659, -1.0368,  0.7162, -1.6902, -0.0592, -0.1070,\n",
       "          -0.6886, -0.7866, -0.7553,  1.4195,  1.5699,  0.9841,  0.4776, -0.3180,\n",
       "           0.9323, -1.1571, -0.6543, -1.0678, -0.7452,  1.8477,  1.1743, -1.7672,\n",
       "          -1.6390, -1.0156,  0.7376,  2.2978, -2.6530,  1.3302, -1.7446, -2.4729,\n",
       "           0.6205,  0.6407,  1.6592, -2.3314, -0.7750,  0.6958,  0.7285,  2.2241,\n",
       "          -1.8953, -0.2656,  0.9911, -0.0504, -0.5359, -0.6441, -0.6024,  0.5280,\n",
       "           0.2150, -0.3437, -1.0493, -0.3169,  0.2424,  0.6939, -0.4680,  0.3454,\n",
       "          -0.8265,  0.7939, -1.6136,  0.7531,  0.7626,  0.4799, -1.0739,  0.6079,\n",
       "           1.8249,  1.8265,  1.7122,  0.5185,  0.5332, -0.1872, -0.2244, -1.5459,\n",
       "           0.4636, -1.5644,  0.1901,  0.5221,  0.4157,  0.7587,  0.7498, -0.1612,\n",
       "           0.5481,  1.2918, -0.6466, -1.1452,  0.3309,  0.6696,  0.5661, -0.8253,\n",
       "          -0.9229,  0.3166, -0.5563,  0.3423,  1.3204,  0.0349,  1.5071, -0.3032,\n",
       "           0.9722,  0.1780,  1.6022,  0.6773,  0.1800, -0.9473, -0.8067,  0.6278,\n",
       "           1.0620, -0.7729, -1.6639,  0.2878, -0.9021,  1.6630, -0.3487,  1.6592,\n",
       "           0.8066, -1.4964, -0.2438,  0.7275,  0.0222,  1.0478,  1.0262, -0.0890,\n",
       "          -0.2321,  1.5273, -0.3918,  0.7217, -1.0287, -0.7648,  0.3073, -1.1039,\n",
       "          -0.3481,  0.4861,  0.2785,  0.1792, -0.9319,  1.6061,  0.6281, -0.0154,\n",
       "          -1.1181, -2.5565,  0.2986,  0.9180, -1.3276,  0.6827, -0.0215,  0.8089,\n",
       "          -0.9900, -1.5626,  0.9667, -0.4180,  0.7440,  1.1410, -0.0708, -1.0918,\n",
       "          -0.8042, -0.5438, -0.3236, -0.3962,  1.4715, -1.2177, -0.1389,  0.6124,\n",
       "          -0.3409,  0.9078,  0.4808,  0.5124, -0.8654, -0.6520,  0.3134, -0.2260,\n",
       "          -1.5315,  1.0941, -0.6730, -0.1544, -0.3089,  0.1861, -0.6585,  0.7197,\n",
       "          -1.0547,  0.8729, -0.0799,  2.5122,  0.4702,  0.1960,  0.3011,  0.3577,\n",
       "          -0.1456, -0.5288, -1.2930,  1.5796, -0.3297, -0.3733,  1.3425, -0.6596,\n",
       "          -0.2732, -0.5533,  0.8054,  1.8698,  0.3777,  1.3157,  0.7981, -1.2277,\n",
       "           0.6787, -1.4794, -0.1905, -0.7679,  0.9668, -0.1477,  1.2648, -1.6029,\n",
       "          -0.1077, -0.3084,  0.0801,  0.1506, -0.0053,  1.7386, -1.7711,  0.6437,\n",
       "          -0.3100,  1.3592, -0.0774, -0.7944, -0.3701, -1.4213,  0.1396, -2.2186,\n",
       "           0.4410,  0.2895,  1.0216, -0.2117, -0.5705,  1.4619, -0.3009, -0.2834,\n",
       "          -0.3964, -0.2474, -1.8923,  2.2656, -0.5563, -1.2096,  0.3389, -0.7855,\n",
       "           0.7722, -0.9675,  1.8095, -0.5547, -0.3889, -0.6782, -1.6191, -0.5289,\n",
       "           0.1377,  1.1940,  0.6314,  0.7929,  0.1512,  1.8028,  2.2546,  1.8066,\n",
       "           0.2349, -0.0483,  0.8605, -1.9575,  1.3238, -0.2642,  0.6749, -0.9080,\n",
       "          -0.4145, -0.6289,  1.3396, -0.1194, -0.5150, -0.0676,  0.1520,  0.5805,\n",
       "           0.5786,  0.9194,  0.6945, -1.8152, -0.2235,  0.7920,  0.6791, -0.3259,\n",
       "           0.2468, -1.1325, -0.5203, -0.6627, -0.4381, -0.8142, -1.8248,  0.0113,\n",
       "          -0.6389, -0.7778,  1.0957, -0.5918, -0.6454,  0.4395, -0.3218, -0.4940,\n",
       "           0.6376, -0.8661, -0.3718, -0.3185, -0.0036,  0.1648, -0.2078,  0.1925,\n",
       "          -0.8006,  0.6722,  0.9937, -0.8720,  0.1078, -0.8192, -0.9418, -1.6691,\n",
       "           1.0833,  1.5117, -0.3932, -0.0391, -0.3180, -1.0979, -0.9085,  0.9619,\n",
       "           1.6197, -0.5967,  0.7030, -0.3899, -0.4394,  0.6991,  0.1401, -0.5728,\n",
       "          -0.4798, -0.0614, -1.0233,  1.2081,  1.0077, -0.8043,  2.4617, -0.6124,\n",
       "           0.7084, -0.4173, -0.7823, -0.6055, -0.6277,  0.4018, -0.0576, -0.1732,\n",
       "           0.3018, -0.8596,  0.9723, -0.7424, -0.7714,  0.6192, -0.8365,  0.1010,\n",
       "           0.0459, -0.1466,  0.7169,  0.2708,  0.4189, -1.1354,  0.6668,  1.3190,\n",
       "           1.7623, -1.0237, -0.1605, -1.1208,  0.9961,  0.2362,  0.8415, -0.7164,\n",
       "           0.2385,  2.1420,  0.3173, -1.1138, -0.2503, -0.2157,  0.7040,  1.7104,\n",
       "           2.3700, -0.2500,  0.7711,  0.0827,  0.8287,  1.7750, -0.2812, -0.8804,\n",
       "           0.7540,  2.0184,  0.6471,  2.8339, -0.9830, -0.9925,  0.1628,  0.1786,\n",
       "           0.0725, -0.7115, -0.1948, -0.9048,  0.4189, -1.0584, -0.5648, -1.4114,\n",
       "          -1.2469,  0.5943, -1.2949, -0.1693,  0.3102, -0.0655,  1.3148,  0.1695]],\n",
       "        grad_fn=<IndexBackward0>))"
      ]
     },
     "execution_count": 278,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from transformers import CLIPTokenizer\n",
    "\n",
    "tokenizer = CLIPTokenizer.from_pretrained(\"openai/clip-vit-base-patch32\")\n",
    "\n",
    "text = \"a red cat\"\n",
    "inputs = tokenizer(text, return_tensors=\"pt\")\n",
    "input_ids = inputs[\"input_ids\"]\n",
    "\n",
    "config = CLIPTextConfig(\n",
    "    vocab_size=49408,\n",
    "    hidden_size=768,\n",
    "    intermediate_size=3072,\n",
    "    num_hidden_layers=12,\n",
    "    num_heads=12,\n",
    "    max_position_embeddings=77,\n",
    "    layer_norm_eps=1e-5,\n",
    "    attention_dropout=0.0,\n",
    ")\n",
    "\n",
    "encoder = CLIPTextEncoder(config)\n",
    "\n",
    "encoder.encode(input_ids)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
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  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.6"
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 },
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